A comprehensive vehicle braking model for predictions of stopping distances

Delaigue, Pierre; Eskandarian, Azim

doi:10.1243/0954407042707641

Cited by 32 publications

(19 citation statements)

References 7 publications

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“…The total braking gain was chosen as 98.2 Nm/kPa for all the three configurations (Fancher et al, 1986;Cao et al, 2007). The sum of the driver's reaction time and the braking system time lag was set as 0.75 s, while the rise time of the braking system was set as 0.25 s (Fancher et al, 1986;Delaigue and Eskandarian, 2004). The initial vehicle speed for the analyses was set as 100 km/h, while the braking input was selected as 172 kPa for all the three vehicle configurations considered.…”

Section: Pitch Attitude Responses Under Braking Inputsmentioning

confidence: 99%

Heavy vehicle pitch dynamics and suspension tuning. Part I: unconnected suspension

Cao

Rakheja

2008

Vehicle System Dynamics

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The influence of suspension tuning of passenger cars on bounce and pitch ride performance has been explored in a number of studies, while only minimal efforts have been made for establishing similar rules for heavy vehicles. This study aims to explore pitch dynamics and suspension tunings of a two-axle heavy vehicle with unconnected suspension, which could also provide valuable information for heavy vehicles with coupled suspensions. Based on a generalized pitch plane model of a two-axle heavy vehicle integrating either unconnected or coupled suspension, three dimensionless measures of suspension properties are defined and analyzed, namely the pitch margin (PM), pitch stiffness ratio (PSR) and coupled pitch stiffness ratio (CPSR), for different unconnected suspension tunings and load conditions. Dynamic responses of the vehicle with three different load conditions and five different tunings of the unconnected suspension are obtained under excitations arising from three different random road roughness conditions and a wide range of driving speeds, and braking maneuvers. The responses are evaluated in terms of performance measures related to vertical and pitch ride, dynamic tire load, suspension travel, and pitch attitude control characteristics of the vehicle. Fundamental relationships between the vehicle responses and the proposed suspension measures (PM, PSR and CPSR) are established, based on which some basic suspension tuning rules for heavy vehicles with unconnected suspensions are also proposed.

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Section: Pitch Attitude Responses Under Braking Inputsmentioning

confidence: 99%

Heavy vehicle pitch dynamics and suspension tuning. Part I: unconnected suspension

Cao

Rakheja

2008

Vehicle System Dynamics

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“…This is related to the vehicle model, road condition and real-time speed at the moment braking is applied. In most of real systems and for simplicity SD is calculated instead of stopping time [22]. Therefore, SD is adopted in this paper and the relationship between the stopping distance and time are calculated in (25).…”

Section: Fusion Algorithm-based V2v Collision Avoidance Systemmentioning

confidence: 99%

GNSS/Electronic Compass/Road Segment Information Fusion for Vehicle-to-Vehicle Collision Avoidance Application

Sun

Cheng

Xue

et al. 2017

Sensors

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The increasing number of vehicles in modern cities brings the problem of increasing crashes. One of the applications or services of Intelligent Transportation Systems (ITS) conceived to improve safety and reduce congestion is collision avoidance. This safety critical application requires sub-meter level vehicle state estimation accuracy with very high integrity, continuity and availability, to detect an impending collision and issue a warning or intervene in the case that the warning is not heeded. Because of the challenging city environment, to date there is no approved method capable of delivering this high level of performance in vehicle state estimation. In particular, the current Global Navigation Satellite System (GNSS) based collision avoidance systems have the major limitation that the real-time accuracy of dynamic state estimation deteriorates during abrupt acceleration and deceleration situations, compromising the integrity of collision avoidance. Therefore, to provide the Required Navigation Performance (RNP) for collision avoidance, this paper proposes a novel Particle Filter (PF) based model for the integration or fusion of real-time kinematic (RTK) GNSS position solutions with electronic compass and road segment data used in conjunction with an Autoregressive (AR) motion model. The real-time vehicle state estimates are used together with distance based collision avoidance algorithms to predict potential collisions. The algorithms are tested by simulation and in the field representing a low density urban environment. The results show that the proposed algorithm meets the horizontal positioning accuracy requirement for collision avoidance and is superior to positioning accuracy of GNSS only, traditional Constant Velocity (CV) and Constant Acceleration (CA) based motion models, with a significant improvement in the prediction accuracy of potential collision.

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“…Maneuver timing has been studied by many researchers, in terms of the reaction time from a stimulus to an evasive reaction, and reaction time estimates have been proposed as functions of a range of parameters: Stimulus eccentricity, number of obstacles, nighttime versus daytime driving, age, gender, as well as the above-mentioned cognitive distraction and stimulus expectancy (Delaigue & Eskandarian, 2004;Green, 2000;Muttart, 2003;B. Wang, Abe, & Kano, 2002).…”

Section: Critical Collision Avoidancementioning

confidence: 99%

“…Many researchers have mainly been interested in the details of control in near-crash and crash phases, and have thus not needed to provide any account of why these states were reached in the first place (see e.g. Delaigue & Eskandarian, 2004;Jurecki & Stańczyk, 2009, and the large body of work on avoidance by steering). Other researchers have included in their scope also the low risk and conflict driving states, and have therefore needed to incorporate mechanisms causing transitions to the more critical states: Either visual distraction behavior (see the section on visual distraction, as well as e.g.…”

Section: Putting the Reviewed Models To Usementioning

confidence: 99%